Dominant component suppression with applications to spectral analysis

This paper describes a novel approach to background suppression termed dominant component suppression (DCS) that extends the basic concept in two ways. First, DCS adapts (via unsupervised clustering) to the major backgrounds or dominant components in the scene and significantly reduces the spectral signal from these interferers. This process can bring the dataset closer to the often assumed multivariate normality and the simple data model of target and white noise. Second, the DCS algorithm produces residual spectra that have the same spectral dimensions and radiometric units as the input data thus facilitating spectroscopic exploitation of the data. This experiment uses an airborne visible infrared spectrometer (AVIRIS) hyperspectral image corrected to reflectance, which has a spectral target material linearly embedded at a range of sub-pixel abundances. While artificial, this technique provides accurate ground truth for this exploratory experiment. Spectral angle mapper was applied to the reflectance and residual images and the results indicate that exploitation can be dramatically enhanced when conducted in residual space by reducing false alarms.